Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells1351
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.5 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2218/S/1/datasetView.do

Alerts

이산화질소농도(ppm) is highly overall correlated with 오존농도(ppm) and 3 other fieldsHigh correlation
오존농도(ppm) is highly overall correlated with 이산화질소농도(ppm)High correlation
일산화탄소농도(ppm) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
초미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
이산화질소농도(ppm) has 170 (1.7%) missing valuesMissing
오존농도(ppm) has 184 (1.8%) missing valuesMissing
일산화탄소농도(ppm) has 234 (2.3%) missing valuesMissing
아황산가스농도(ppm) has 179 (1.8%) missing valuesMissing
미세먼지농도(㎍/㎥) has 295 (2.9%) missing valuesMissing
초미세먼지농도(㎍/㎥) has 289 (2.9%) missing valuesMissing
아황산가스농도(ppm) is highly skewed (γ1 = 27.67946095)Skewed

Reproduction

Analysis started2024-05-04 06:05:48.628017
Analysis finished2024-05-04 06:06:14.489848
Duration25.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct365
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20180683
Minimum20180101
Maximum20181231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:14.778437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180101
5-th percentile20180121
Q120180405
median20180707
Q320181009
95-th percentile20181215
Maximum20181231
Range1130
Interquartile range (IQR)604

Descriptive statistics

Standard deviation348.79012
Coefficient of variation (CV)1.7283365 × 10-5
Kurtosis-1.2283278
Mean20180683
Median Absolute Deviation (MAD)302
Skewness-0.041096579
Sum2.0180683 × 1011
Variance121654.55
MonotonicityNot monotonic
2024-05-04T06:06:15.352873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181113 40
 
0.4%
20181223 38
 
0.4%
20181114 37
 
0.4%
20181117 36
 
0.4%
20181229 36
 
0.4%
20180131 36
 
0.4%
20181202 35
 
0.4%
20181211 35
 
0.4%
20181116 35
 
0.4%
20181106 34
 
0.3%
Other values (355) 9638
96.4%
ValueCountFrequency (%)
20180101 28
0.3%
20180102 21
0.2%
20180103 27
0.3%
20180104 24
0.2%
20180105 22
0.2%
20180106 27
0.3%
20180107 21
0.2%
20180108 28
0.3%
20180109 26
0.3%
20180110 26
0.3%
ValueCountFrequency (%)
20181231 32
0.3%
20181230 31
0.3%
20181229 36
0.4%
20181228 32
0.3%
20181227 32
0.3%
20181226 29
0.3%
20181225 25
0.2%
20181224 29
0.3%
20181223 38
0.4%
20181222 30
0.3%

측정소명
Categorical

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
구로구
 
266
홍릉로
 
264
강남대로
 
262
금천구
 
259
동작대로
 
258
Other values (41)
8691 

Length

Max length4
Median length3
Mean length3.2337
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정릉로
2nd row영등포로
3rd row광진구
4th row청계천로
5th row노원구

Common Values

ValueCountFrequency (%)
구로구 266
 
2.7%
홍릉로 264
 
2.6%
강남대로 262
 
2.6%
금천구 259
 
2.6%
동작대로 258
 
2.6%
중구 258
 
2.6%
강동구 258
 
2.6%
관악구 257
 
2.6%
서대문구 256
 
2.6%
성동구 256
 
2.6%
Other values (36) 7406
74.1%

Length

2024-05-04T06:06:15.961462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구로구 266
 
2.7%
홍릉로 264
 
2.6%
강남대로 262
 
2.6%
금천구 259
 
2.6%
동작대로 258
 
2.6%
중구 258
 
2.6%
강동구 258
 
2.6%
관악구 257
 
2.6%
서대문구 256
 
2.6%
성동구 256
 
2.6%
Other values (36) 7406
74.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)1.0%
Missing170
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.032568566
Minimum0.001
Maximum0.129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:16.576846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.012
Q10.021
median0.031
Q30.042
95-th percentile0.06
Maximum0.129
Range0.128
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.015071233
Coefficient of variation (CV)0.46275396
Kurtosis0.54218646
Mean0.032568566
Median Absolute Deviation (MAD)0.011
Skewness0.7231539
Sum320.149
Variance0.00022714206
MonotonicityNot monotonic
2024-05-04T06:06:17.198698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 272
 
2.7%
0.022 267
 
2.7%
0.021 263
 
2.6%
0.018 263
 
2.6%
0.017 262
 
2.6%
0.025 254
 
2.5%
0.027 253
 
2.5%
0.029 252
 
2.5%
0.023 251
 
2.5%
0.024 248
 
2.5%
Other values (86) 7245
72.5%
ValueCountFrequency (%)
0.001 4
 
< 0.1%
0.003 4
 
< 0.1%
0.004 1
 
< 0.1%
0.005 10
 
0.1%
0.006 17
 
0.2%
0.007 29
 
0.3%
0.008 46
 
0.5%
0.009 79
0.8%
0.01 89
0.9%
0.011 133
1.3%
ValueCountFrequency (%)
0.129 1
 
< 0.1%
0.115 1
 
< 0.1%
0.114 1
 
< 0.1%
0.105 1
 
< 0.1%
0.103 1
 
< 0.1%
0.095 2
 
< 0.1%
0.094 1
 
< 0.1%
0.093 4
< 0.1%
0.09 2
 
< 0.1%
0.089 5
0.1%

오존농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)0.7%
Missing184
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean0.020873166
Minimum0
Maximum0.074
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:17.787610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.011
median0.019
Q30.028
95-th percentile0.042
Maximum0.074
Range0.074
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.011843
Coefficient of variation (CV)0.5673792
Kurtosis0.37473333
Mean0.020873166
Median Absolute Deviation (MAD)0.008
Skewness0.71585347
Sum204.891
Variance0.00014025666
MonotonicityNot monotonic
2024-05-04T06:06:18.481437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018 358
 
3.6%
0.007 319
 
3.2%
0.009 313
 
3.1%
0.008 307
 
3.1%
0.017 302
 
3.0%
0.014 301
 
3.0%
0.015 301
 
3.0%
0.016 299
 
3.0%
0.01 298
 
3.0%
0.023 297
 
3.0%
Other values (61) 6721
67.2%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
0.002 50
 
0.5%
0.003 155
1.6%
0.004 237
2.4%
0.005 259
2.6%
0.006 249
2.5%
0.007 319
3.2%
0.008 307
3.1%
0.009 313
3.1%
0.01 298
3.0%
ValueCountFrequency (%)
0.074 1
 
< 0.1%
0.07 2
 
< 0.1%
0.069 4
< 0.1%
0.068 2
 
< 0.1%
0.067 1
 
< 0.1%
0.066 4
< 0.1%
0.065 1
 
< 0.1%
0.064 5
0.1%
0.063 5
0.1%
0.062 5
0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)0.2%
Missing234
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.54854598
Minimum0.1
Maximum2.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:18.908627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.4
median0.5
Q30.7
95-th percentile1
Maximum2.7
Range2.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.22621677
Coefficient of variation (CV)0.41239345
Kurtosis4.067819
Mean0.54854598
Median Absolute Deviation (MAD)0.1
Skewness1.2752923
Sum5357.1
Variance0.051174025
MonotonicityNot monotonic
2024-05-04T06:06:19.341402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.4 2004
20.0%
0.5 1949
19.5%
0.6 1484
14.8%
0.3 1343
13.4%
0.7 961
9.6%
0.8 701
 
7.0%
0.9 419
 
4.2%
0.2 333
 
3.3%
1.0 259
 
2.6%
1.1 135
 
1.4%
Other values (13) 178
 
1.8%
(Missing) 234
 
2.3%
ValueCountFrequency (%)
0.1 30
 
0.3%
0.2 333
 
3.3%
0.3 1343
13.4%
0.4 2004
20.0%
0.5 1949
19.5%
0.6 1484
14.8%
0.7 961
9.6%
0.8 701
 
7.0%
0.9 419
 
4.2%
1.0 259
 
2.6%
ValueCountFrequency (%)
2.7 1
 
< 0.1%
2.6 1
 
< 0.1%
2.5 1
 
< 0.1%
2.3 1
 
< 0.1%
2.0 4
 
< 0.1%
1.8 2
 
< 0.1%
1.7 2
 
< 0.1%
1.6 2
 
< 0.1%
1.5 13
0.1%
1.4 24
0.2%

아황산가스농도(ppm)
Real number (ℝ)

MISSING  SKEWED 

Distinct16
Distinct (%)0.2%
Missing179
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean0.0044915996
Minimum0.001
Maximum0.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:19.810067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.004
Q30.005
95-th percentile0.007
Maximum0.14
Range0.139
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.002098699
Coefficient of variation (CV)0.46724979
Kurtosis1769.2701
Mean0.0044915996
Median Absolute Deviation (MAD)0.001
Skewness27.679461
Sum44.112
Variance4.4045374 × 10-6
MonotonicityNot monotonic
2024-05-04T06:06:20.401790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.004 2302
23.0%
0.005 2205
22.1%
0.003 2153
21.5%
0.006 1330
13.3%
0.002 740
 
7.4%
0.007 647
 
6.5%
0.008 256
 
2.6%
0.009 81
 
0.8%
0.001 64
 
0.6%
0.01 29
 
0.3%
Other values (6) 14
 
0.1%
(Missing) 179
 
1.8%
ValueCountFrequency (%)
0.001 64
 
0.6%
0.002 740
 
7.4%
0.003 2153
21.5%
0.004 2302
23.0%
0.005 2205
22.1%
0.006 1330
13.3%
0.007 647
 
6.5%
0.008 256
 
2.6%
0.009 81
 
0.8%
0.01 29
 
0.3%
ValueCountFrequency (%)
0.14 1
 
< 0.1%
0.016 1
 
< 0.1%
0.015 1
 
< 0.1%
0.013 2
 
< 0.1%
0.012 4
 
< 0.1%
0.011 5
 
0.1%
0.01 29
 
0.3%
0.009 81
 
0.8%
0.008 256
 
2.6%
0.007 647
6.5%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct150
Distinct (%)1.5%
Missing295
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean42.250902
Minimum3
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:20.971062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q124
median38
Q354
95-th percentile91
Maximum204
Range201
Interquartile range (IQR)30

Descriptive statistics

Standard deviation24.706113
Coefficient of variation (CV)0.58474759
Kurtosis1.857839
Mean42.250902
Median Absolute Deviation (MAD)15
Skewness1.1927349
Sum410045
Variance610.39201
MonotonicityNot monotonic
2024-05-04T06:06:21.849439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 217
 
2.2%
30 209
 
2.1%
26 195
 
1.9%
36 190
 
1.9%
41 189
 
1.9%
38 187
 
1.9%
39 184
 
1.8%
31 183
 
1.8%
25 182
 
1.8%
35 181
 
1.8%
Other values (140) 7788
77.9%
(Missing) 295
 
2.9%
ValueCountFrequency (%)
3 6
 
0.1%
4 12
 
0.1%
5 31
 
0.3%
6 44
 
0.4%
7 59
0.6%
8 63
0.6%
9 99
1.0%
10 85
0.9%
11 126
1.3%
12 122
1.2%
ValueCountFrequency (%)
204 1
< 0.1%
167 1
< 0.1%
165 1
< 0.1%
163 1
< 0.1%
161 1
< 0.1%
159 1
< 0.1%
153 2
< 0.1%
150 2
< 0.1%
148 1
< 0.1%
147 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)1.2%
Missing289
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean23.738853
Minimum1
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:06:22.564847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median20
Q331
95-th percentile54
Maximum138
Range137
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.985072
Coefficient of variation (CV)0.67337171
Kurtosis4.0255713
Mean23.738853
Median Absolute Deviation (MAD)9
Skewness1.5881863
Sum230528
Variance255.52253
MonotonicityNot monotonic
2024-05-04T06:06:23.191552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 319
 
3.2%
18 317
 
3.2%
9 314
 
3.1%
17 313
 
3.1%
20 306
 
3.1%
22 299
 
3.0%
16 288
 
2.9%
10 288
 
2.9%
21 285
 
2.9%
11 281
 
2.8%
Other values (104) 6701
67.0%
(Missing) 289
 
2.9%
ValueCountFrequency (%)
1 17
 
0.2%
2 41
 
0.4%
3 120
 
1.2%
4 153
1.5%
5 207
2.1%
6 254
2.5%
7 278
2.8%
8 279
2.8%
9 314
3.1%
10 288
2.9%
ValueCountFrequency (%)
138 1
< 0.1%
124 1
< 0.1%
122 1
< 0.1%
121 2
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
116 1
< 0.1%
114 1
< 0.1%
112 2
< 0.1%
108 2
< 0.1%

Interactions

2024-05-04T06:06:09.866761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:54.351270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:56.849463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:59.600601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:02.226015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:04.854714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:06.952546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:10.238482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:54.678696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:57.250556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:59.886485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:02.777543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:05.138096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:07.236498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:10.637913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:54.985491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:57.677713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:00.312221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:03.359909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:05.409117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:07.679467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:10.987302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:55.386705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:58.163326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:00.693145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:03.663067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:05.691437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:08.109738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:11.467856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:55.773148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:58.458862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:01.035261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:03.974762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:05.958965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:08.519161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:11.848448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:56.089360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:58.787527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:01.410968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:04.258595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:06.260862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:08.930106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:12.339375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:56.435107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:05:59.259207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:01.773018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:04.544990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:06.561990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:06:09.370632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:06:23.549957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.2620.4340.6200.5030.0300.5440.501
측정소명0.2621.0000.4860.3550.6200.0720.2010.129
이산화질소농도(ppm)0.4340.4861.0000.5400.6740.0000.6040.608
오존농도(ppm)0.6200.3550.5401.0000.5310.0000.3090.324
일산화탄소농도(ppm)0.5030.6200.6740.5311.0000.0000.5660.570
아황산가스농도(ppm)0.0300.0720.0000.0000.0001.0000.0000.000
미세먼지농도(㎍/㎥)0.5440.2010.6040.3090.5660.0001.0000.898
초미세먼지농도(㎍/㎥)0.5010.1290.6080.3240.5700.0000.8981.000
2024-05-04T06:06:24.054083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)측정소명
측정일시1.000-0.092-0.208-0.007-0.199-0.237-0.2160.093
이산화질소농도(ppm)-0.0921.000-0.5040.6920.4150.6450.6430.187
오존농도(ppm)-0.208-0.5041.000-0.484-0.242-0.185-0.1590.129
일산화탄소농도(ppm)-0.0070.692-0.4841.0000.3890.6290.6320.256
아황산가스농도(ppm)-0.1990.415-0.2420.3891.0000.4710.4480.035
미세먼지농도(㎍/㎥)-0.2370.645-0.1850.6290.4711.0000.8980.070
초미세먼지농도(㎍/㎥)-0.2160.643-0.1590.6320.4480.8981.0000.044
측정소명0.0930.1870.1290.2560.0350.0700.0441.000

Missing values

2024-05-04T06:06:13.017869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:06:13.676298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-04T06:06:14.180498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
962320180904정릉로0.030.0250.30.0042315
642220180614영등포로0.0430.0150.40.006<NA><NA>
402520180414광진구0.0140.0330.50.0032111
432520180421청계천로0.0630.0240.60.0088150
98620180126노원구0.0160.0260.50.0063420
423420180419송파구0.0320.0590.60.0068842
835220180803공항대로0.0530.0370.40.0094639
355920180402금천구0.0270.0270.40.0056424
1030720180922노원구0.0210.0230.40.004299
1171920181028서초구0.0240.0140.30.003156
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
1164620181026양천구0.0490.0160.50.0033216
822220180730중구0.0210.0210.30.002106
722120180705공항대로0.040.0130.40.0062818
599520180603은평구0.0220.0460.30.0033124
928120180826화랑로0.0220.0220.30.0032312
832420180802마포구<NA><NA><NA><NA><NA><NA>
552620180522용산구0.030.0230.40.0033022
451120180426영등포로0.0690.0170.50.0076039
830120180801중랑구0.020.0440.40.0052618
1324220181201영등포구0.0370.0070.80.0055120